sric: Sharpe Ratio Information Coefficient

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Computes the Sharpe Ratio Information Coefficient of Paulsen and Soehl, an asymptotically unbiased estimate of the out-of-sample Sharpe of the in-sample Markowitz portfolio.

Usage

1
sric(z.s)

Arguments

z.s

an object of type sropt

Details

Let X be an observed T x k matrix whose rows are i.i.d. normal. Let mu and Sigma be the sample mean and sample covariance. The Markowitz portfolio is

w = Sigma^-1 mu,

which has an in-sample Sharpe of zeta = sqrt(mu' Sigma^-1 mu).

The Sharpe Ratio Information Criterion is defined as

SRIC = zeta - ((k-1) / (T zeta)).

The expected value (over draws of X and of future returns) of the SRIC is equal to the expected value of the out-of-sample Sharpe of the (in-sample) portfolio w (again, over the same draws.)

Value

The Sharpe Ratio Information Coefficient.

Author(s)

Steven E. Pav shabbychef@gmail.com

References

Paulsen, D., and Soehl, J. "Noise Fit, Estimation Error, and Sharpe Information Criterion." arxiv preprint (2016): http://arxiv.org/abs/1602.06186

See Also

Other sropt Hotelling: inference

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
# generate some sropts
nfac <- 3
nyr <- 5
ope <- 253
# simulations with no covariance structure.
# under the null:
set.seed(as.integer(charToRaw("fix seed")))
Returns <- matrix(rnorm(ope*nyr*nfac,mean=0,sd=0.0125),ncol=nfac)
asro <- as.sropt(Returns,drag=0,ope=ope)
srv <- sric(asro)


Search within the SharpeR package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.